



import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_lowercase    	=		get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
   _lowercase    	=		json.load(f)



@require_torch
class      _UpperCAmelCase      (							unittest.TestCase  ):




     def 		snake_case_		(					self			,    a__):
         return FSMTTokenizer.from_pretrained(a__)




     def 		snake_case_		(					self			,    a__):
         A__					=       FSMTForConditionalGeneration.from_pretrained(a__).to(a__)
         if torch_device == "cuda":
             model.half()
         return model




     @parameterized.expand(
         [
             ['''en-ru''', 2_6.0],
             ['''ru-en''', 2_2.0],
             ['''en-de''', 2_2.0],
             ['''de-en''', 2_9.0],
         ])
     @slow
     def 		snake_case_		(					self			,    a__			,    a__):
         # note: this test is not testing the best performance since it only evals a small batch
         # but it should be enough to detect a regression in the output quality
         A__					=       F"facebook/wmt19-{pair}"
         A__					=       self.get_tokenizer(a__)
         A__					=       self.get_model(a__)

         A__					=       bleu_data[pair]['''src''']
         A__					=       bleu_data[pair]['''tgt''']

         A__					=       tokenizer(a__			,    return_tensors='''pt'''			,    truncation=a__			,    padding='''longest''').to(a__)
         A__					=       model.generate(
             input_ids=batch.input_ids			,    num_beams=8			,    )
         A__					=       tokenizer.batch_decode(
             a__			,    skip_special_tokens=a__			,    clean_up_tokenization_spaces=a__)
         A__					=       calculate_bleu(a__			,    a__)
         print(a__)
         self.assertGreaterEqual(scores['''bleu''']			,    a__)






'''simple docstring'''


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate





@dataclass(frozen=__magic_name__			)
class  __lowercase    (	__magic_name__			):
   _a    						=   field(default="""audio-classification"""							,				metadata={"""include_in_asdict_even_if_is_default""": True}			)
   _a    						=   Features({"""audio""": Audio()}			)
   _a    						=   Features({"""labels""": ClassLabel}			)
   _a    						=   "audio"
   _a    						=   "labels"







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Dict:
     if self.label_column not in features:
       raise ValueError(f"Column {self.label_column} is not present in features."   )
     if not isinstance(features[self.label_column]   ,				UpperCamelCase   ):
       raise ValueError(f"Column {self.label_column} is not a ClassLabel."   )
     __a								=       copy.deepcopy(self   )
     __a								=       self.label_schema.copy()
     __a								=       features[self.label_column]
     __a								=       label_schema
     return task_template







   @property
   def 		UpperCamelCase__      (						self   )					->     Dict[str, str]:
     return {
         self.audio_column: "audio",
         self.label_column: "labels",
     }



